Our architecture, depicted in Figure REF  and used for both CNNs, is composed of 10 convolutional (conv) layers, each followed by Rectified Liner Units (ReLU) [1]} activations. We decided to use ReLU activations, as this represents the most popular choice of transfer function in current research based on deep learning. All convolutional layers contain filters with a spatial support of \(3\times 3\) . While older deep models were based on larger filters, e.g. the AlexNet [2]} architecture contains filters of \(11\times 11\) , the recent trend is towards using smaller filters, e.g. the ResNet [3]} architecture does not contain filters larger than \(3\times 3\) .
